Innovation corner

When it comes to complex and costly automotive manufacturing processes, using reliable simulation tools to supplement expensive prototyping and physical testing has proven to be a very efficient method of optimizing the design stage. Therefore, the automotive industry is constantly looking for appropriate solutions for it. In the case of e-coating, this includes optimizing the various design parameters like tank dimensions, vehicle trajectory, line speed, and geometrical details of the vehicle body also known as Body in White (BIW). CFD tools, such as PreonLab, that provide not only high accuracy but also lean workflows and short computational times, are highly beneficial for this purpose. The ultimate goal is to gain reliable insights into how design parameters affect the process and to optimize these parameters. PreonLab provides powerful in-built post-processing tools for this purpose, including a wide range of options such as wetting, force, volume, and pathlines sensors. In this article, we will look at the e-coating process and show the capabilities of PreonLab in simulating and optimizing the fluid-dynamic aspects of this process.

In this article, we look at an application from the maritime industry for a breaking dam flow and the wave impact on an obstacle placed in the flow. Initially, a series of uniform resolution simulations were performed with PreonLab 5.1 to determine the particle size necessary for accurate simulation results. Subsequently, simulations have also been performed with PreonLab 5.2 to make use of the new Continuous Particle Size (CPS) feature and analyze the benefits this feature provides towards reducing computational effort without compromising on the accuracy of the results. The simulation results obtained in PreonLab are compared qualitatively and quantitatively with experiment results published in the paper by Kleefsman et.al [1]. The experiments were performed at the Maritime Research Institute Netherlands (MARIN). All the experimental data generated is also available to download from the ERCOFTAC database [2].

The latest PreonLab release for version 5.2 introduced some exciting new features such as Continuous Particle Size (CPS), sensor planes for the solid solver, and heat field. In this article, we will look at the benefits of these features for thermodynamic applications with the example of electric motor cooling. There is indeed a large range of applications across different industries which make use of electric motors for power generation. The focus of this article will be on electric motors in the context of electric vehicles.

When it comes to complex production processes in the automotive industry like e-coating, using reliable CFD simulation tools to supplement expensive prototyping and physical testing has proven to be a very efficient method to optimize the design stage. This includes optimizing the various design parameters like tank dimensions, vehicle trajectory, line speed and geometrical details of the Body in White (BIW). For this purpose, CFD tools providing not only high accuracy, but also lean workflows and short computational times are required.

We at FIFTY2 configure and maintain a few dozen physical and virtualized servers for our developers and application engineers, so they have a solid, basic infrastructure to develop, test and simulate on. Getting everyone to remember one single password for everything is easy, but certainly not a best practice in operational security. Also, writing down every password in a shared spreadsheet still feels kind of wrong, but leads into the right direction. There are plenty of available password managers to choose from, be it online as a service, offline, shared with other people, or just integrated into the browser you are using right now to read this text. Chosing one of them is no big deal, but what if it comes to automatically accessing those machines that we set up, with passwords that are stored somewhere in a password manager? And how can we gain access to a server when physically standing in front of it, in case a disaster hits the fan? How can different people stay on top of all passwords configured, without reusing a password, ever? And, once we overcome those challenges, what other handy things can we do with such a system? None of those challenges are new or extremely hard to solve problems, but getting them set up initially and making them work smoothly can have some bumps down the road. In this article, we show how we are using password-store to manage various credentials for multiple systems, how we set it up to couple it with the Ansible automation platform. In case you are interested in trying this out yourself, there should be enough code snippets to get you up and running in no time.

With our recent release of PreonLab 5.2, which was a very special release for us, we introduced our new feature – Continuous Particle Size (CPS), where particles can have any size in a user defined range. In this article, we shall take a look at the development of adaptive refinement and coarsening in PreonLab and the benefits of CPS along with the challenges and limitations that go with it.

We are excited to announce the release of PreonLab 5.2. One reason is that as FIFTY2 we are intrigued by the number 52. But more importantly, PreonLab 5.2 is a huge leap forward for PreonLab and particle-based CFD. It is packed with features that improve reliability, performance and usability across a wide range of applications. Here are some of the highlights: Continuous Particle Size (CPS):  With CPS, each particle stores its own individual size. This new flexibility allows for greater speedups when using refinement and coarsening. Multiphase: Achieve higher accuracy in demanding multiphase simulations without tuning. Thermodynamics: Enhanced sensor capabilities and a new temperature-based density implementation will give you more options for your simulations setups. Car Suspension with AVL VSM: With PreonLab 5.2, a new suspension model is made available in PreonLab as part of the the Vehicle Simulation Model or VSM. This is possible by combining the power of PreonLab with AVL VSM.   Usability & Workflow: Import CAD files such as STEP and convert them to the blistering fast and lightweight PRMESH format. Furthermore, enjoy the improved readability of graphs using adjustable axis labels in the Plot Dialog. This is just a selection of new features and improvements. Check out the changelog to learn about all changes or login with your AVL account to watch the PreonLab 5.2 Release Event on demand. Make sure to follow us on LinkedIn so that you don’t miss new videos, case studies and updates!

This is more than an arbitrary release! Join us to celebrate the release of PreonLab 5.2 and be the first to learn about the new features. Just click on the “Go to recording!” button below to watch the release event in full! PRESENTERS: Jens Cornelis, Markus Ihmsen, Loïc Wendling, Aju Abraham LANGUAGE: English

In this test case, the thermal conduction through a wall made of multiple materials is analyzed. The analytical solution of the benchmark found in Lewis et al. [1] will be used to validate the thermal conduction and Conjugated Heat Transfer (CHT) in PreonLab. Furthermore, different thermal boundary conditions and the heterogenous spacing feature that allows you to choose different spacings for each solver are tested as well.

Preoneers all over the globe know, that there is no such thing as an ordinary PreonLab release. But this time, we have something very special for you. Not only because the FIFTY2 engineers are numberphiles and version 5.2 is special for us for obvious reasons. But also because PreonLab 5.2 will push things to the limit, starting with Continuous Particle Size giving more degrees of freedom and a significantly improved workflow. But there is even more. Don‘t miss the news and join us for this event!